A License Plate Recognition System (LPRS) is a system to automatically detect, recognize and identify a vehicle plate.
Vehicle license plate (VLP) constitutes an unambiguous identifier of a vehicle participating in road traffic. Reading a license plate is the first step in determining the identities of parties involved in traffic incidents.
License-Plate Recognition System consists of three main modules:
License plate detection,
character segmentation and
Optical Character Recognition (OCR).
Flow chart Start Input Image License Plate Extraction Character Segmentation Character Identification Display Plate Number End
License Plate Detection
Preprocessing : T he first step is to identify the regions in the image that contain the intensity of RGB corresponding to the color yellow
Morphological operation : These are Non-linear filters, with the function of restraining noises, extracting features and segmenting images etc
Horizontal segmentation : It will give the width and the x coordinates of the potentially candidates regions.
Vertical segmentation : It will give the Height and the y coordinates of the potentially candidates regions
Identifying License Plate : T wo features are defined and extracted in order to decide if a candidate region contains a license plate or not , these features are:
1. Aspect ratio
2. Edge Density
Conversion to gray scale.
Optical Character Recognition(OCR)
Training : The program is first trained with a set of sample images for each of the characters to extract the important features based on which the recognition operation would be performed.
The program must be trained on a set of 10 characters with 10 samples of each. The training algorithm involves the following steps:
Template matching : C alculate the matching score of the segmented character from the templates of the character stored by the following algorithm.
Compare the pixel values of the matrix of segmented character and the template matrix, and for every match we add 1 to the matching score and for every mis-match we decrement 1. This is done for all 225 pixels. The match score is generated for every template and the one which gives the highest score is taken to be the recognized character
computerized road traffic monitoring systems
electronic fee collection solutions,
safety supervision systems
Many difference solutions have already been proposed for each stage of recognition
Use edge statistics to locate the plate
Fuzzy clustering algorithms
Optical Character Recognition
Automatic Vehicle License-Plate Recognition System By Deepak Kumar Gupta-Y6154 and Siddhartha Kandoi-Y6472
Combining Hough Transform and Contour Algorithm for detecting Vehicles. License-Plates - Tran Duc Duan, Duong Anh Duc, Tran Le Hong Du – October 2004
Building an Automatic Vehicle License-Plate Recognition System - Tran Duc Duan, Tran Le Hong Du, Tran Vinh Phuoc, Nguyen Viet Hoang – Febraury 2005
A High Accurate Macau License Plate Recognition System - CheokMan , Kengchung - 2008
Rectangular Object Tracking Based on Standard Hough Transform - Thuy Tuong Nguyen, Xuan Dai Pham and Jae Wook Jeon, - February, 2009
A New Algorithm for Character Segmentation of License Plate - Yungang Zhang Changshui Zhang - June 2003
Mean Shift for Accurate Number Plate Detection - Wenjing Jia, Huaifeng Zhang, and Xiangjian He - July 2005